ER-Swin: Feature Enhancement and Refinement Network Based on Swin Transformer for Semantic Segmentation of Remote Sensing Images

As the field of remote sensing image processing continues to advance, semantic segmentation has become a focal point in this domain. The emergence of the swin transformer (SwinT) has greatly alleviated the computational complexities associated with transformers, leading to its widespread application in the field of semantic segmentation. However, most current network models lack a feature enhancement process internally, and the model’s tail lacks refinement modules to prevent category misjudgments caused by feature redundancy. To address this issue, we propose ER-Swin to explore the potential of utilizing SwinT as the backbone network for semantic segmentation in remote sensing images. Addressing the need for feature enhancement in the backbone network, we propose interactive feature enhancement attention (IFEA), which leverages diagonal information interaction to augment features. Additionally, we design the semantic selective refinement module (SSRM) to refine the rich features at the tail end of the network, thereby enhancing segmentation outcomes. We evaluated our model on the Vaihingen, Potsdam, and LoveDA datasets and achieved accuracies of 84.89%, 87.20%, and 55.1%, respectively, on the mean intersection over union (mIoU) metric. Through comparative experiments, we demonstrate the superior segmentation performance of our model, affirming its competitiveness.